Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism

2020 ◽  
Vol 191 ◽  
pp. 105237 ◽  
Author(s):  
Hammoudi Abderazek ◽  
Ali Riza Yildiz ◽  
Seyedali Mirjalili
Author(s):  
Adel A. Younis ◽  
George H. Cheng ◽  
G. Gary Wang ◽  
Zuomin Dong

Metamodel based design optimization (MBDO) algorithms have attracted considerable interests in recent years due to their special capability in dealing with complex optimization problems with computationally expensive objective and constraint functions and local optima. Conventional unimodal-based optimization algorithms and stochastic global optimization algorithms either miss the global optimum frequently or require unacceptable computation time. In this work, a generic testbed/platform for evaluating various MBDO algorithms has been introduced. The purpose of the platform is to facilitate quantitative comparison of different MBDO algorithms using standard test problems, test procedures, and test outputs, as well as to improve the efficiency of new algorithm testing and improvement. The platform consists of a comprehensive test function database that contains about 100 benchmark functions and engineering problems. The testbed accepts any optimization algorithm to be tested, and only requires minor modifications to meet the test-bed requirements. The testbed is useful in comparing the performance of competing algorithms through execution of same problems. It allows researchers and practitioners to test and choose the most suitable optimization tool for their specific needs. It also helps to increase confidence and reliability of the newly developed MBDO tools. Many new MBDO algorithms, including Mode Pursuing Sampling (MPS), Pareto Set Pursuing (PSP), and Space Exploration and Unimodal Region Elimination (SEUMRE), were tested in this work to demonstrate its functionality and benefits.


2013 ◽  
Vol 5 (4) ◽  
Author(s):  
Paulo Flores

The main objective of this work is to present a computational approach for design optimization of disc cam mechanisms with eccentric translating roller followers. For this purpose, the objective function defined here takes into account the three major parameters that influence the final cam size, namely, the base circle radius of the cam, the radius of the roller and the offset of the follower. Furthermore, geometric constraints related to the maximum pressure angle and minimum radius of curvature are included to ensure good working conditions of the system. Finally, an application example is presented and used to discuss the main assumptions and procedure adopted throughout this work.


Author(s):  
Christopher Chahine ◽  
Joerg R. Seume ◽  
Tom Verstraete

Aerodynamic turbomachinery component design is a very complex task. Although modern CFD solvers allow for a detailed investigation of the flow, the interaction of design changes and the three dimensional flow field are highly complex and difficult to understand. Thus, very often a trial and error approach is applied and a design heavily relies on the experience of the designer and empirical correlations. Moreover, the simultaneous satisfaction of aerodynamic and mechanical requirements leads very often to tedious iterations between the different disciplines. Modern optimization algorithms can support the designer in finding high performing designs. However, many optimization methods require performance evaluations of a large number of different geometries. In the context of turbomachinery design, this often involves computationally expensive Computational Fluid Dynamics and Computational Structural Mechanics calculations. Thus, in order to reduce the total computational time, optimization algorithms are often coupled with approximation techniques often referred to as metamodels in the literature. Metamodels approximate the performance of a design at a very low computational cost and thus allow a time efficient automatic optimization. However, from the experiences gained in past optimizations it can be deduced that metamodel predictions are often not reliable and can even result in designs which are violating the imposed constraints. In the present work, the impact of the inaccuracy of a metamodel on the design optimization of a radial compressor impeller is investigated and it is shown if an optimization without the usage of a metamodel delivers better results. A multidisciplinary, multiobjective optimization system based on a Differential Evolution algorithm is applied which was developed at the von Karman Institute for Fluid Dynamics. The results show that the metamodel can be used efficiently to explore the design space at a low computational cost and to guide the search towards a global optimum. However, better performing designs can be found when excluding the metamodel from the optimization. Though, completely avoiding the metamodel results in a very high computational cost. Based on the obtained results in present work, a method is proposed which combines the advantages of both approaches, by first using the metamodel as a rapid exploration tool and then switching to the accurate optimization without metamodel for further exploitation of the design space.


2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm


2018 ◽  
Vol 6 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Mostafa Jalal ◽  
Maral Goharzay

Abstract In the present study, Cuckoo Search (CS) as a nature-inspired optimization algorithm was applied for structural and design optimization of a new float system for experimental setups. For this purpose, based on the setup configuration, it was tried to minimize the total length of the float, while maintaining the structural and performance-based constraints. Different geometries for the float structure were examined to come up with the feasible options. The problem was formulated into a constrained optimization in terms of four or five variables, depending on the geometry, along with two performance-based constraints and some structural constraints. CS was used to solve the constrained optimization problem and the convergence trends of the parameters to optimal solutions were examined in details. Generalized reduced gradient (GRG) method known as GRG nonlinear was also used for validation and comparison purpose. The results of the optimization and the performance of the float produced showed that CS can be used as a powerful tool for applied structural and design problems. It should be mentioned that the float problem can be used as a benchmark structural design problem for validation of new optimization algorithms. Besides, the optimal float can be produced for various experimental setups with different structures and constraints, depending on the application. Highlights Cuckoo Search (CS) algorithm as a metaheuristic approach. Constrained optimization in structural design using CS algorithm. Designing a new float for experimental setups. Production of an optimal float for measurement system. Float design as a benchmark problem for optimization algorithms.


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